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scTDA / single-cell Topological Data Analysis
Serves for topology-based computational analyses. scTDA realizes temporal studies and unbiased transcriptional regulation studies. It is an unsupervised statistical framework that can characterize transient cellular states. This tool can be used to any biological system responding to inductive cues or environmental perturbations like cellular differentiation processes such as hematopoiesis, the evolution of cancer cells, neurodegeneration, or developmental disorders.
MAST / Model-based Analysis of Single-cell Transcriptomics
A flexible statistical framework for the analysis of single-cell RNA sequencing data. MAST is suitable for supervised analyses about differential expression of genes and gene modules, as well as unsupervised analyses of model residuals, to generate hypotheses regarding co-expression of genes. MAST accounts for the bimodality of single-cell data by jointly modeling rates of expression (discrete) and positive mean expression (continuous) values. Information from the discrete and continuous parts is combined to infer changes in expression levels using gene or gene set-based statistics. Because our approach uses a generalized linear framework, it can be used to jointly estimate nuisance variation from biological and technical sources, as well as biological effects of interest.
Allows quality control (QC) and analysis components of parallel single cell transcriptome and epigenome data. Dr.seq is a quality control (QC) and analysis pipeline that provides both multifaceted QC reports and cell clustering results. Parallel single cell transcriptome data generated by different technologies can be transformed to the standard input with contained functions. Using relevant commands, the software can also be used to report quality measurements based on four aspects and can generate detailed analysis results for scATAC-seq and Drop-ChIP datasets.
SinQC / Single-cell RNA-seq Quality Control
A method and software tool to detect technical artifacts in single-cell RNA-seq (scRNA-seq) samples by integrating both gene expression patterns and data quality information. SinQC assumes that if gene expression outliers are also associated with poor sequencing library quality (poor data quality, e.g., low mapped reads, low mapping rate or low library complexity), then they are more likely to be technical artifacts than to be cells with real biological variation. We apply SinQC to nine different scRNA-seq datasets, and show that SinQC is a useful tool for controlling scRNA-seq data quality.
Makes analysis more broadly accessible to researchers. Granatum is a web browser based scRNAseq analysis pipeline that conveniently walks the users through various steps of scRNA-seq analysis. It has a comprehensive list of modules, including plate merging and batch effect removal, outlier sample removal, gene filtering, gene expression normalization, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein network interaction visualization, and pseudo-time cell series construction.
SINCERA / SINgle CEll RNA-seq profiling Analysis
A generally applicable analytic pipeline for processing single-cell RNA-seq data from a whole organ or sorted cells. SINCERA provides a panel of analytic tools for users to conduct data filtering, normalization, clustering, cell type identification, and gene signature prediction, transcriptional regulatory network construction and important regulatory node identification. The pipeline enables RNA-seq analysis from heterogeneous single cell preparations after the nucleotide sequence reads are aligned to the genome of interest.
A method to correct for cell growth in single-cell transcriptomics data. We derive the probability for the cell growth corrected mRNA transcript number given the measured, cell size dependent mRNA transcript number, based on the assumption that the average number of transcripts in a cell increases proportional to the cell's volume during cell cycle. cgCorrect can be used for both data normalization, and to analyze steady-state distributions used to infer the gene expression mechanism.
Sharq / Single-cell Hierarchical Assignment of Reads and Quality control
Offers a method for managing 3’- end unique molecular identifiers (UMI)-based protocols. Sharq first removes and sorts low quality reads, maps the cleaned files to a reference genome and then performs a specific assignation that generates gene expression tables. The application is able to deal with UMIs and cell barcodes. It can be used for detecting wells where the amplification reaction failed, or to evaluate which cells contained sufficient material relative to an empty well background.
PIVOT / Platform for Interactive analysis and Visualization Of Transcriptomics data
Allows users to analyze and visualize RNA-Seq data. PIVOT furnishes four mains functionalities (i) a graphical interface that is able to wrap existing open source packages in a single user-interface (ii) multiple tools to manipulate datasets to perform derivation or normalization (iii) a way for allowing the compatibility between inputs and outputs from different analysis modules and, (iv) functions for automatically generate reports, publication-quality figures, and reproducible computations.
MAGIC / Markov Affinity-based Graph Imputation of Cells
Provides a method for imputing missing values, and restoring the structure of the data. After the use of MAGIC, two- and three-dimensional gene interactions are restored. MAGIC is able to impute complex and non-linear shapes of interactions. MAGIC also retains cluster structure, enhances cluster-specific gene interactions and restores trajectories, as demonstrated in mouse retinal bipolar cells, hematopoiesis, and a generated epithelial-to-mesenchymal transition dataset.
Contains useful tools for the analysis of single-cell gene expression data using the statistical software R. scater places an emphasis on tools for quality control, visualisation and pre-processing of data before further downstream analysis. scater enables the following: (i) automated computation of QC metrics; (ii) transcript quantification from read data with pseudo-alignment; (iii) data format standardisation; (iv) rich visualisations for exploratory analysis; (v) seamless integration into the Bioconductor universe; (vi) simple normalisation methods.
Visualizes transcriptome (RNA expression) data from hundreds of samples. Flotilla is a Python package. Flotilla is an open source, community-driven software written in Python that enables biologists with rudimentary knowledge of statistical methods and programming to analyze and visualize hundreds of RNA-seq datasets. This package includes interactive functions for common and important tasks in computational analyses of biological datasets such as dimensionality reduction, covariance analysis, classification, regression and outlier detection.
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